Building Renewal Copenhagen

Introduction

The script in this R Markdown document is a part of a spatial project exploring the topic of building renewal in Copenhagen.

The script in this document does the following:

Creates a map showing the buildings in Copenhagen which have undergone building renewal.
Transforms and combines two CSV files showing the number of buildings older than 1899 per district.
Combines this data with spatial data showing the different districts of Copenhagen. Makes an accompanying map.

Data used

The data used has been created by Copenhagen Municipality.

Københavns Kommunes Statistikbank. “KKBYG1.” Accessed August 11, 2023. https://kk.statistikbank.dk/statbank5a/SelectVarVal/Define.asp?MainTable=KKBYG1&PLanguage=0&PXSId=0&wsid=cflist.
Open Data DK. “Bydele.” Accessed August 11, 2023, https://www.opendata.dk/city-of-copenhagen/bydele.
Open Data DK. “Byfornyelse.” Accessed August 11, 2023. https://www.opendata.dk/city-of-copenhagen/byfornyelse#resource-byfornyelse.
Open Data DK. “Udsatte byområder.” Accessed August 11, 2023. https://www.opendata.dk/city-of-copenhagen/udsatte-byomrader.

License: Attribution 4.0 International (CC BY 4.0): https://creativecommons.org/licenses/by/4.0/

Loading Packages

Firstly, I need to load the packages for this spatial project. The following packages have been used:

tidyverse (1.3.0)
sf (1.0-6) for processing the spatial data
rgdal (1.5-28) for reading spatial data
leaflet (2.1.0) for creating the maps.

library(tidyverse) # (version 1.3.0)

Error : 'format_warning' is not an exported object from 'namespace:cli'

library(sf) # for reading spatial data (version 1.0-6)
library(rgdal) # also for reading spatial data (version 1.5-28)
library(leaflet) # for making the maps (version 2.1.0)

Building Renewal and Vulnerable Areas

Reading the spatial data

I’m going to read the spatial data downloaded from Open Data DK by using the readLines function. (resource consulted: https://rstudio.github.io/leaflet/json.html )

# loading the spatial file showing renewed buildings
building_renewal <-  readLines("data/byfornyelse_kk.json") %>%
  paste(collapse = "\n")

# loading the spatial file showing vulnerable areas
vulnerable_areas <-  readLines("data/f_udsatte_byomraader.json") %>%
  paste(collapse = "\n")

Creating the Leaflet map

Then I’m create the map using Leaflet.

leaflet() %>%
      addTiles() %>% # adding basic tiles
      setView(lng = 12.52840564444299, lat = 55.658326021508024, zoom = 11) %>% #setting coordinates
      addGeoJSON(building_renewal, weight = 2, fillOpacity = 1) %>% # adding geojson feature
      addGeoJSON(vulnerable_areas, color = "#ED2828", weight = 1) # adding second geojson feature

Building Renewal and Building Ages

Loading data

District map

The first file I will need to load is the geojson file from Open Data DK showing the different districts of Copenhagen. I am reading it using rgdal this time since I am going to combine it with non spatial data. By using rgdal I also get access to more functions of customization. (resource consulted: https://rstudio.github.io/leaflet/json.html )

districts <- rgdal::readOGR("data/bydel.json")

OGR data source with driver: GeoJSON 
Source: "/Users/rie/Desktop/building_renewal_CPH/data/bydel.json", layer: "bydel"
with 10 features
It has 5 fields

Here is the spatial data showing the city districts on its own:

Ages of buildings

Next, I will load the CSV files showing the ages of the buildings.

# CSV file containing the number of buildings older than 1850
bfr1850 <-  read.csv2("data/before_1850.csv")

# CSV containing the number of buildings fra 1850-1899
from1850to1899 <-  read.csv2("data/1850-1899.csv")

Data wrangling

Selecting the year 2023

The CSV files contains information on the number of buildings registered from 1991 to 2023. I have chosen to use 2023 since it appears like there was a lot of building registration done in 2017, and I want the data to be as complete as possible.

I am going the use the filter function to only include that year.

bfr1850 <- filter(bfr1850, TID == "2023") # selecting column TID (time) and selecting year 2023

from1850to1899 <- filter(from1850to1899, TID == "2023")

Selecting the rows showing the districts

The CSV file contains a lot of additional information besides the information on the ten districts. I am going to select the rows where the name start with “bydel” (district)

(tutorial consulted: https://stackoverflow.com/questions/28860069/regular-expressions-regex-and-dplyrfilter )

bfr1850 <- bfr1850 %>%
  # selecting the column OMRKK (area CPH) and choosing the rows starting with "bydel" (district)
  filter(str_detect(OMRKK, "Bydel")) %>%
  # there is an additional row called "outside districts" which I need to remove
  filter(OMRKK != "Bydel - Uden for inddeling")

from1850to1899 <- from1850to1899 %>%
  filter(str_detect(OMRKK, "Bydel")) %>% 
  filter(TID == "2023") %>% 
  filter(OMRKK != "Bydel - Uden for inddeling")

Matching the name formating

I need to make sure that the names in the data frame is the same as in the geojson so that I can combine them later on.

I am changing the name of the “OMRKK” column in the dataframe to match the “navn” (name) column in the geojson

bfr1850 <-rename(bfr1850, navn = OMRKK)

from1850to1899 <- rename(from1850to1899, navn = OMRKK)

Additionally, This means that I have to remove the “Bydel -” part from the names in the data frame.

bfr1850$navn[1:10] <-  c("Indre By", "Østerbro", "Nørrebro", "Vesterbro-Kongens Enghave", "Valby", "Vanløse", "Brønshøj-Husum", "Bispebjerg", "Amager Øst", "Amager Vest")

from1850to1899$navn[1:10] <-  c("Indre By", "Østerbro", "Nørrebro", "Vesterbro-Kongens Enghave", "Valby", "Vanløse", "Brønshøj-Husum", "Bispebjerg", "Amager Øst", "Amager Vest")

Combining the two data frames

At the moment, I have two seperate data frames and I want to combine them using the function inner_join().

bfr1899 <- inner_join(bfr1850, from1850to1899, 
           by = c("navn", "ENHED", "TID")) # choosing the columns that match

I’m also adding the “INDHOLD” (contents) column together so that it shows the number of buildings before 1899.

(Tutorial consulted: https://dplyr.tidyverse.org/reference/mutate.html)

bfr1899 <-  bfr1899 %>% mutate(combined = INDHOLD.x + INDHOLD.y) 
# NB: naming the column "combined"

Combining the two datasets

The last step before creating the map is to combine the two datasets using the merge function. (tutorial consulted: https://stackoverflow.com/questions/46695816/merge-csv-and-json-file-in-leaflet-map-strange-mistake)

# Combining by using the "navn" (name) column which the two data sets now have in common.
bfr1899_map <- merge(districts, bfr1899, by = "navn")

Creating the Leaflet map

I’m going to create a palette so that the different districts are shaded according to the continuous data.

palette <- colorBin("Blues", bfr1899_map$combined, 10, pretty=TRUE)

Now I can create the second map:

leaflet(bfr1899_map) %>%
  addTiles() %>%
  setView(lng = 12.52840564444299, lat = 55.658326021508024, zoom = 10) %>% 
  addPolygons(weight = 1,
              fillColor = ~palette(bfr1899_map$combined), # using palette from before
              fillOpacity = 0.75,
              label = ~paste0(navn, ": ", formatC(combined, big.mark = ","))) %>%
  addLegend("bottomright", pal = palette, values = ~bfr1899_map$combined, 
            opacity = 1,
            title = "Number of buildings older than 1899 by district") %>% 
  addGeoJSON(building_renewal, weight = 2, fillOpacity = 1, color = "orange")

References

Data Used

Københavns Kommunes Statistikbank. “KKBYG1.” Accessed August 8, 2023. https://kk.statistikbank.dk/statbank5a/SelectVarVal/Define.asp?MainTable=KKBYG1&PLanguage=0&PXSId=0&wsid=cflist.
Open Data DK. “Bydele.” Accessed August 8, 2023, https://www.opendata.dk/city-of-copenhagen/bydele.
Open Data DK. “Byfornyelse.” Accessed August 8, 2023. https://www.opendata.dk/city-of-copenhagen/byfornyelse#resource-byfornyelse.
Open Data DK. “Udsatte byområder.” Accessed August 8, 2023. https://www.opendata.dk/city-of-copenhagen/udsatte-byomrader.

License for data used

Attribution 4.0 International (CC BY 4.0): https://creativecommons.org/licenses/by/4.0/

Tutorials and Internet Resources Consulted

dplyr. “Create, modify, and delete columns.” Accessed August 10. https://dplyr.tidyverse.org/reference/mutate.html
Leaflet for R. “Working with GeoJSON & TopoJSON.” Accessed July 31, 2023. https://rstudio.github.io/leaflet/json.html.
StackOverflow. “Regular expressions (RegEx) and dplyr::filter().” Accessed August 10. 2023. https://stackoverflow.com/questions/28860069/regular-expressions-regex-and-dplyrfilter
StackOverflow. “Merge csv and json file in Leaflet map - strange mistake.” Accessed August 10. https://stackoverflow.com/questions/46695816/merge-csv-and-json-file-in-leaflet-map-strange-mistake